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A Deep Dive – From Theory to Practice, the Bible for 700,000 AI Learners

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2025-10-20

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Dive into Deep Learning

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As deep learning transitions from academic circles to industry, countless learners face a dilemma: “understanding theory but not coding, or knowing how to code but lacking grasp of principles.” They shrink from thick math textbooks, yet struggle to build systematic knowledge from scattered online tutorials. However, Dive into Deep Learning, co-authored by AI experts like Mu Li and Aston Zhang, breaks down the “dual barriers” of deep learning education with its unique model of “theory and code advancing hand in hand.” By 2025, the book has accumulated 700,000 readers worldwide, been adopted as a textbook by over 200 universities (including Tsinghua University and Peking University), and become internal training material for companies like ByteDance and Huawei. Drawing on the book’s content, practical cases, and reader feedback, this article unpacks the core logic behind its status as “the No.1 introductory book on deep learning.”

I. Book Positioning: More Than a “Tutorial” – A Complete Deep Learning Practice System

Dive into Deep Learning is not a traditional “theory book” or “code manual”; it is a comprehensive learning system integrating “mathematical principles, algorithm derivation, code implementation, and project practice.” In the preface, author Mu Li (Chief Scientist at Amazon, Visiting Professor at UC Berkeley) clarifies its positioning: “We want readers to pick up this book and not only understand ‘what it is,’ but also personally implement ‘how to do it’ – truly transforming deep learning from ‘knowledge’ into ‘competence.’”

The book’s core competitiveness stems from three key features:

Academic Rigor: Developed in collaboration with experts from institutions like Stanford University and the University of Michigan, it covers everything from basic mathematics (linear algebra, probability theory) to core algorithms (CNN, RNN, Transformer). Every formula derivation undergoes repeated verification to ensure theoretical depth and cutting-edge relevance;

Practice-Oriented Approach: The entire book is supplemented with PyTorch code (with TensorFlow implementations in some editions). Each algorithm chapter follows the logic of “principle explanation → code breakdown → result verification,” allowing readers to code while learning and observe the model’s operation in real time;

Open-Source Accessibility: The book’s electronic version and supporting code are fully free (available on the official d2l.ai website), with both Chinese and English editions to support barrier-free learning for global readers. The Chinese community alone has accumulated over 100,000 reader notes and Q&A entries.

II. Content Structure: A Four-Stage Closed-Loop for Deep Learning Cognition

Following the logic of “from basics to advanced, from general to specialized,” the book is divided into four parts, totaling approximately 800 pages. It caters to learners at all levels (from beginners to advanced), with a recommended study cycle of 3–6 months (1–2 hours per day).

1. Fundamentals Section: Building the “Foundation” of Deep Learning

Tailored for beginners, this section explains “essential knowledge for deep learning entry” in plain language, avoiding overwhelming readers with complex formulas upfront. Core content includes:

Mathematical Foundations: Using the “housing price prediction” case to explain the mathematical principles of linear regression, and the “image classification” scenario to illustrate gradient descent algorithms. Abstract concepts like vector and matrix operations are transformed into “perceivable problem-solving processes,” making them accessible even to readers with weak calculus backgrounds;

Tool Introduction: A detailed guide to PyTorch’s core functions (installation, tensor operations, automatic differentiation), paired with a step-by-step example of “building your first neural network.” It breaks down the entire process from “importing libraries → defining the model → training data → evaluating performance,” enabling readers to run their first deep learning program in just 30 minutes;

Core Concepts: Clear differentiation between easily confused terms (e.g., “machine learning vs. deep learning,” “supervised vs. unsupervised learning,” “overfitting vs. underfitting”). Practical questions like “Why is the ReLU activation function more commonly used than Sigmoid?” guide readers to think about the logic behind technologies.

The highlight of this section is its “beginner-friendliness”: the authors deliberately avoid academic jargon – for example, using “feeding data to the model” instead of “data input,” and “the model has learned biased patterns” to explain overfitting – making it easy for non-computer science majors to get started.

2. Algorithms Section: Mastering the “Core Technologies” of Deep Learning

As the “core chapter” of the book, this section focuses on mainstream algorithms like CNN, RNN, and Transformer. Each chapter follows a four-step logic: “principle → derivation → implementation → optimization.” Typical chapters include:

Convolutional Neural Networks (CNNs): Starting with “Why do we need CNNs?” (solving the problem of excessive parameters in fully connected networks), it uses “sliding windows” to analogize convolution operations. Through the “handwritten digit recognition” project (MNIST dataset), readers implement a CNN model, then explore optimization ideas for classic networks like ResNet and Inception;

Recurrent Neural Networks (RNNs): Using “text generation” as a scenario, it explains the “temporal memory” characteristics of RNNs and compares how LSTMs and GRUs solve the “long-sequence gradient vanishing” problem. The supporting “Tang poetry generation” project allows readers to train models and generate AI-written poems that follow traditional metrics;

Transformers and Attention Mechanisms: As the “key advanced content” of the book, it uses “focusing on key words in translation” to analogize the attention mechanism, gradually deriving the Transformer’s encoder-decoder structure. Through the “English-Chinese translation” project (IWSLT dataset), readers understand the underlying logic of large models like BERT and GPT.

Each algorithm chapter includes “annotated code” and “frequently asked questions (FAQs).” For example, in the Transformer chapter, the authors explicitly note practical pitfalls like “Why split dimensions in multi-head attention?” and “Why does the order of layer normalization matter?” to help readers avoid common mistakes.

3. Advanced Section: Addressing “Practical Challenges” in Deep Learning

After mastering basic algorithms, this section focuses on “solving problems in real industrial scenarios,” with content closer to actual work needs:

Model Optimization Techniques: Covering practical methods like batch normalization, learning rate scheduling, and regularization. Through experiments comparing “model performance with different optimization strategies,” readers visually see “how to make models train faster and perform better”;

Data Processing Methods: Addressing common issues like “small dataset size” and “poor data quality,” it introduces techniques such as data augmentation (image flipping, text synonym replacement) and transfer learning. The supporting “image classification with small datasets” project demonstrates how transfer learning improves model performance;

Basic Model Deployment: A brief introduction to deployment-related knowledge (ONNX format conversion, model quantization), with an example of “deploying a trained model to a local computer” to help readers understand “the final step from ‘training a model’ to ‘putting it into use.’”

Cases in this section are mostly derived from real industrial needs. One reader shared: “After learning the model optimization chapter, I reduced the training time of my company’s recommendation system model from 2 days to 8 hours, while increasing accuracy by 5% – directly solving a key business pain point.”

4. Applications Section: Unlocking “Industry Scenarios” for Deep Learning

To help readers apply technologies to specific fields, the book selects three popular directions – computer vision, natural language processing (NLP), and recommendation systems – each with “key technologies + complete projects”:

Computer Vision: Covering tasks like image classification, object detection, and image segmentation. Using the “mask detection” project (based on the YOLO model), it breaks down the entire practical process of object detection, from data annotation to model training and result visualization;

Natural Language Processing: Including applications like text classification, sentiment analysis, and machine translation. The supporting “e-commerce review sentiment analysis” project teaches readers to use BERT models to “automatically identify positive/negative reviews” and generate visualized word cloud analysis results;

Recommendation Systems: Introducing core technologies like collaborative filtering and matrix factorization. Through the “movie recommendation” project (based on the MovieLens dataset), readers personally implement the function of “recommending personalized movies to different users” and understand key issues like “cold start” in recommendation systems.

These projects provide complete dataset download links and code repositories, allowing readers to directly reproduce them. Some excellent projects can even be included in job portfolios – an HR manager at an internet company noted: “When we see ‘reproduced the recommendation system project from Dive into Deep Learning’ on a resume, we prioritize arranging interviews, as this proves the candidate has solid practical abilities.”

III. Core Advantages: Four Traits That Set It Apart from Similar Books

Among similar works like Deep Learning (by Goodfellow et al.) and Advanced Deep Learning, Dive into Deep Learning stands out due to four irreplaceable advantages:

1. “Learn by Doing”: Say Goodbye to “Understanding ≠ Being Able to Apply”

Traditional books often “attach a section of code after explaining theory,” leaving readers stuck in the trap of “understanding code when reading, but struggling to write it independently.” In contrast, this book embeds “code snippets + annotations” on almost every page. For example, when explaining linear regression, it first provides code for “defining the model,” then explains line-by-line “why this definition works” and “what each parameter means.” Readers can run the code locally simultaneously, modifying parameters in real time to observe changes (e.g., adjusting the learning rate to see how it affects model convergence speed). One reader commented: “With other books, I understood the formulas but couldn’t write the code; after following this book and typing code myself, I not only learned to write it but also understood ‘why it’s written this way.’”

2. Open-Source and Free: Lowering the Barrier to Learning

The book’s electronic version (bilingual), supporting code, and course PPTs are all freely available on the official d2l.ai website – no payment required to access complete resources. Additionally, the author team maintains a GitHub repository (with over 60,000 stars), regularly updating code (to adapt to the latest PyTorch versions) and FAQs. They even provide “study roadmaps” (e.g., “which chapters should beginners read first,” “which parts to focus on for algorithm job interviews”) to help readers avoid detours. For students or learners on a budget, this “zero-cost access to high-quality resources” model is highly appealing.

3. Community Support: A “Mutual-Aid Ecosystem” of 700,000 Readers

The Chinese community built around the book has become a key learning support system:

Q&A Support: On the d2l.ai forum or the Zhihu topic “Dive into Deep Learning,” readers typically receive responses to their questions within 12 hours, with some even answered personally by author Mu Li;

Resource Sharing: Community users voluntarily compile “key chapter notes,” “formula derivation flashcards,” and “project practice videos.” For example, one user’s notes on “line-by-line breakdown of Transformer code” have been downloaded over 50,000 times;

Study Check-Ins: Regular “30-Day Deep Learning Check-In” events are held, where participants share daily progress and challenges, creating a “mutual motivation” atmosphere. Many readers noted: “Following the community check-ins, I finally persisted in finishing this thick book.”

4. Continuous Iteration: Keeping Pace with Technological Frontiers

The author team updates the book’s content annually to align with industry technology trends: a “large model fine-tuning” chapter was added in 2023, “efficient fine-tuning technologies like LoRA and QLoRA” were supplemented in 2024, and “foundations of multimodal models” (e.g., image-text generation, speech recognition) were included in 2025. This “living book” model ensures readers do not learn “outdated knowledge.”

IV. Target Audience: Who Should Read This Book?

Dive into Deep Learning is not a “one-size-fits-all” book, but it offers exceptional value for three groups:

1. Beginners: A Springboard from “Novice” to “Practitioner”

If you are a student majoring in computer science or mathematics, or a professional looking to switch to AI – and meet the prerequisites of “knowing basic Python (able to write simple functions) and understanding high school mathematics (basic derivatives, matrices)” – this book will help you systematically get started. A mechanical engineering student shared: “I started with zero foundation, and after 3 months, I used the CNN model from the book to complete my course project on ‘part defect detection’ and even got an internship offer for an algorithm position at ByteDance.”

2. University Students/Instructors: High-Quality “Teaching Materials and Resources”

Currently, over 200 universities in China have adopted it as a textbook for courses like “Deep Learning” and “Artificial Intelligence,” for two key reasons:

For Students: Supporting code and projects reduce “stuck points when doing assignments.” An instructor in a university’s computer science department noted: “Previously, students spent a lot of time debugging code for assignments; with this book, they can focus more on understanding algorithm principles”;

For Instructors: The official website provides free course PPTs, homework banks, exam outlines, and even “teaching videos” (with Mu Li explaining key chapters), significantly reducing lesson preparation pressure.

3. Industry Professionals: A “Reference Book” for Solving Practical Problems

For AI engineers, data analysts, and other professionals, this book is a “must-have desk reference”:

When facing model tuning issues, they can refer to the “model optimization chapter” for solutions;

When taking on projects in new fields (e.g., switching from NLP to recommendation systems), they can quickly grasp core technologies through the “applications section”;

They can even use the book’s projects as a basis for “technical research.” For example, an algorithm engineer at a company used the “movie recommendation” project as a prototype to build an internal “document recommendation system” in just 2 weeks.

V. Study Guide: Tips for Avoiding Pitfalls and Advancement Paths

1. Efficient Learning Suggestions

Strengthen Foundations Before Deepening: If your Python or math skills are weak, spend 1–2 weeks studying a “Python basics tutorial” (e.g., Python Crash Course) and “core high school math knowledge” (focusing on derivatives, matrices, and probability) before starting this book. This avoids “being discouraged by stuck points”;

Code While Reading – Do Not Copy: Never directly copy the book’s code. Instead, write it yourself while following the explanations. When encountering errors, first try debugging (e.g., printing tensor shapes, checking parameter dimensions); only consult the community if you cannot solve the problem independently. This improves your “error-solving abilities”;

Learn with “Project-Driven Goals”: After finishing the fundamentals section, set a small goal (e.g., “using CNN for cat vs. dog classification”) and learn subsequent chapters with this goal in mind. For example, to achieve the goal, you need to learn CNN principles, data processing, and model training – this “problem-oriented” approach boosts learning efficiency.

2. Pitfall Avoidance Reminders

Do Not Rush to “Cover Everything”: The book contains a large amount of content – do not aim to “finish it in a week.” Instead, study 1–2 sections per day, and use mind maps to organize knowledge points (e.g., “CNN development timeline: LeNet → AlexNet → ResNet”) after each study session. This prevents “forgetting what you learned earlier”;

Value Mathematical Principles, But Do Not Obsess Over “Derivation Details”: For non-research-oriented readers, focus on understanding “the core idea of algorithms” (e.g., CNN’s “local perception”) and “code implementation logic” – there is no need to dwell on complex mathematical derivations (e.g., every step of matrix differentiation). This avoids “math anxiety”;

Update Code Versions Timely: PyTorch updates quickly. If you encounter “code errors,” first check the “version compatibility notes” in the GitHub repository or search the community for “solutions for the corresponding version.” This avoids wasting time on “version incompatibility” issues.

3. Advancement Paths

Theoretical Deepening: After reading this book, dive into mathematical principles by reading Deep Learning (by Goodfellow et al.) or follow academic papers by Mu Li’s team (e.g., research on Transformer optimization);

Technical Specialization: For fields of interest (e.g., large models, reinforcement learning), take specialized courses (e.g., Mu Li’s “Large Model Practice” series on Bilibili) or participate in Kaggle competitions (applying what you learned to solve practical problems);

Engineering Implementation: Learn model deployment technologies (e.g., TensorRT, ONNX Runtime) and try deploying the book’s projects to servers or mobile devices. For example, deploy the “mask detection” model to a Raspberry Pi for real-time detection.

Conclusion: The “Optimal Solution” for Deep Learning – Hiding in “Hands-On Practice”

The success of Dive into Deep Learning essentially stems from addressing the core pain point of deep learning education: “Understanding theory alone is useless; knowing how to code alone is also useless – only the combination of theory and practice leads to true mastery.” For 2025 learners, this book is not just an “introductory textbook,” but a “long-term practical companion”: it helps beginners cross the threshold into deep learning, assists advanced learners in solving practical work problems, and empowers every reader to turn “abstract AI concepts” into “tangible solutions” that drive real-world value.

In an era where AI technology evolves at breakneck speed – with new models, algorithms, and applications emerging monthly – the “hands-on thinking” cultivated by this book becomes even more valuable. It is not just about teaching readers to “use a certain model” or “write a piece of code,” but about equipping them with the ability to “learn new technologies independently” and “adapt to industry changes flexibly.” As one senior AI engineer at Google commented: “What Dive into Deep Learning teaches is not just knowledge, but a ‘learning methodology’ – and that’s the most precious asset for anyone in the AI field.”

For those still hesitant to start their deep learning journey – worried about weak math foundations, lack of coding experience, or confusion about where to begin – this book offers a clear answer: start with the first chapter, write the first line of code, and let “practice” be your guide. After all, the most effective way to master deep learning is not to “read about it,” but to “do it” – and Dive into Deep Learning is the best companion for that journey.

As Mu Li wrote in the book’s afterword: “Every line of code you write, every model you train, and every problem you solve is a step toward mastering AI. We hope this book can be the ‘first step’ for you – a step that leads to endless possibilities in the world of deep learning.”

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